Category: English

Source: The Conversation – USA (2) – By Joel Lisonbee, Senior Associate Scientist, Cooperative Institute for Research in the Environmental Sciences, University of Colorado Boulder

Cattle auctions aren’t often all-night affairs. But in Texas Lake Country in June 2022, ranchers facing dwindling water supplies and dried out pastures amid a worsening drought sold off more than 4,000 animals in an auction that lasted nearly 24 hours – about 200 cows an hour.

It was the height of a drought that has gripped the Southern Plains for the past six years – a drought that is still holding on in much of the region in 2026.

The drought cost the agriculture industry across Kansas, Oklahoma and Texas an estimated US$23.6 billion in lost crops, higher feed costs and selling off cattle from 2020 through 2024 alone. As rangeland dried out, it has also fueled wildfires, including several in Texas in early 2026.

Historically, droughts of this magnitude happen in the Southern Plains about once a decade, but the severe droughts of this century have been lasting longer, leaving water supplies, native rangelands and farms with little time to recover before the next one hits.

Many cattle producers and rangelands were still recovering from a severe 2010-2015 drought when a flash drought hit western Texas in spring 2020, marking the beginning of the current multibillion-dollar, multiyear and multistate drought. Ample spring rainfall in 2025 and severe flooding in central Texas that year weren’t enough to end the drought, and a powerful winter storm in late January 2026 missed the driest parts of the region.

In a recent study with colleagues at the Southern Regional Climate Center and the National Integrated Drought Information System, we assessed the causes and damage from the ongoing drought in the Southern Plains.

We found three key reasons for the enduring drought and its damage: rising temperatures and a La Niña climate pattern; water supply shortages; and lingering economic impacts from the previous drought.

Weather and climate helped drive the drought

The Southern Plains is known to be a hot spot for rapid drought development, and the ongoing drought that started in 2020 is no exception.

Documented “flash droughts” – defined as periods of rapid drought onset or intensification of existing droughts – occurred at least five times in the region from 2020 to 2025. As global temperatures rise and climates warm, research warns that the frequency and severity of flash drought events will increase.

For the southern part of the Southern Plains, winter precipitation is closely linked to the El Niño–Southern Oscillation, a climate pattern that affects weather around the world. Five of the past six years exhibited a La Niña pattern, which typically means the region sees winters that are warmer and drier than normal.

La Niña was likely the primary driver – although not the only driver – of the drought for Texas and southwest Oklahoma, and one of the reasons drought conditions have continued into 2026.

The Southern Plains have a long history with severe droughts. The Dust Bowl of the early 1930s may be the best-known example. But a history with drought doesn’t make it any easier to manage when crops and water supplies dry up.

Deeply rooted water shortages

The heat and dryness since 2020 have left many of the region’s rivers, reservoirs and even groundwater reserves well below average.

San Antonio’s reservoirs all reached record-low levels in 2024 and 2025, as did the Edwards Aquifer, which provides water for roughly 2.5 million people. They were still low as 2026 began. Surface water and groundwater resources across central and western Texas have been depleted to the point that even a few big storms can’t replenish them.

A few major rivers flow into the Southern Plains from other drought-affected regions. Consider the Rio Grande, which begins in Colorado and winds through New Mexico and along Texas’ southern border: Not only has the Lower Rio Grande valley in southern Texas missed out on needed precipitation this winter, so did the Rio Grande headwaters in southern Colorado.

Colorado is facing a snow drought in winter 2026, as is much of the western U.S. If it continues, there will be less snowmelt come summer to feed rivers, such as the Rio Grande, or fill reservoirs. In early February, the Elephant Butte, Amistad and Falcon reservoirs, along the Rio Grande, were only 11%, 34%, and 20% full, respectively.

Lingering economic impacts

Like water supplies, the economy doesn’t just recover when the rains return.

One of the reasons the current drought has been so costly is that parts of the region had not fully recovered from the 2010-2015 drought when the latest one began in 2020. With only a five-year break between droughts, the landscape behaved like someone with an already weakened immune system who caught a cold.

During the 2010-2015 drought, cattle producers in Texas sold off about 20% of the statewide herd as water became scarce and rangeland dried up. Rebuilding a herd after a drought is a slow process. Pasture recovery can take a year or more, and a newborn heifer will take two years to mature and produce her own first calf.

Cattle herds had still not returned to pre-2010 levels when the 2022 drought peak forced another mass sell-off. From 2020 through 2024, Texas’s herd size declined from 13.1 million to 12 million; Oklahoma’s declined from 5.3 million to 4.7 million; and Kansas’ declined from 6.5 million to 6.15 million.

Looking beyond livestock, a large percentage of the Southern Plains’ crops failed in 2022, the peak year of the drought. In Texas, 25% of the corn crop was planted but never harvested, and 45% of the soybean crop was similarly abandoned. A normal season would have yielded a $2.4 billion cotton crop in Texas, but 74% of that crop was abandoned, slashing its value to roughly $640 million.

Ending the Southern Plains drought

Is the end in sight? With La Niña fading in early 2026 and its opposite, El Niño, potentially on the horizon, there’s a chance for wetter conditions that could reduce the drought in the fall and winter months of 2026.

But the Southern Plains still have to get through spring and summer first. Ending a drought like this requires consistent precipitation over several months, and drought conditions are likely to get worse before they get better.

This article, originally published Feb. 9, 2026, has been updated with new wildfires in Texas.

The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

– ref. Sixth year of drought in Texas and Oklahoma leaves ranchers facing wildfires and bracing for another tough year – https://theconversation.com/sixth-year-of-drought-in-texas-and-oklahoma-leaves-ranchers-facing-wildfires-and-bracing-for-another-tough-year-275219

Source: The Conversation – USA – By Gregory P. Magarian, Thomas and Karole Green Professor of Law, Washington University in St. Louis

The president announces an aggressive, controversial policy. Large groups of protesters take to the streets. Government agents open fire and kill protesters.

All of these events, familiar from Minneapolis in 2026, also played out at Ohio’s Kent State University in 1970. In my academic writing about the First Amendment, I have described Kent State as a key moment when the government silenced free speech.

In Minneapolis, free speech has weathered the crisis better, as seen in the protests themselves, the public’s responses – and even the protest songs the two events inspired.

Protests and shootings, then and now

In 1970, President Richard Nixon announced he had expanded the Vietnam War by bombing Cambodia. Student anti-war protests, already fervent, intensified.

In Ohio, Gov. James Rhodes deployed the National Guard to quell protests at Kent State University. Monday, May 4, saw a large midday protest on the main campus commons. Students exercised their First Amendment rights by chanting and shouting at the Guard troops, who dispersed protesters with tear gas before regrouping on a nearby hill.

With the nearest remaining protesters 20 yards from the Guard troops and most more than 60 yards away, 28 guardsmen inexplicably fired on students, killing four students and wounding nine others.

After the killings, the government sought to shift blame to the slain students. Nixon stated: “When dissent turns to violence, it invites tragedy.”

Minneapolis in 2026 presents vivid parallels.

As part of a sweeping campaign to deport undocumented immigrants, President Donald Trump in early January 2026 deployed armed U.S. Immigration and Customs Enforcement and Customs and Border Protection agents to Minneapolis.

Many residents protested, exercising their First Amendment rights by using smartphones and whistles to record and call out what they saw as ICE and CBP abuses. On Jan. 7, 2026, an ICE agent shot and killed activist Renee Good in her car. On Jan. 24, two CBP agents shot and killed protester Alex Pretti on the street.

The government sought to blame Good and Pretti for their own killings.

Different public reactions

After Kent State, amid bitter conservative opposition to student protesters, most Americans blamed the fallen students for their deaths. When students in New York City protested the Kent State shootings, construction workers attacked and beat the students in what became known as the “hard hat riot.” Afterward, Nixon hosted construction union leaders at the White House, where they gave him an honorary hard hat.

In contrast, most Americans believe the Trump administration has used excessive force in Minneapolis. Majorities both oppose the federal agents’ actions against protesters and approve of protesting and recording the agents.

The public response to Minneapolis has made a difference. The Trump administration has announced an end to its immigration crackdown in the Twin Cities. Trump has backed off attacks on Good and Pretti. Congressional opposition to ICE funding has grown. Overall public support for Trump and his policies has fallen.

Free speech in protests, recordings and songs

What has caused people to view the killings in Minneapolis so differently from Kent State? One big factor, I believe, is how free speech has shaped the public response.

The Minneapolis protests themselves have sent the public a more focused message than what emerged from the student protests against the Vietnam War.

Anti-war protests in 1970 targeted military action on the other side of the world. Organizers had to plan and coordinate through in-person meetings and word of mouth. Student protesters needed the institutional news media to convey their views to the public.

In contrast, the anti-ICE protests in Minneapolis target government action at the protesters’ doorsteps. Organizers can use local networks and social media to plan, coordinate and communicate directly with the public. The protests have succeeded in deepening public opposition to ICE.

In addition, the American people have witnessed the Minneapolis shootings.

Kent State produced a famous photograph of a surviving student’s anguish but only hazy, chaotic video of the shootings.

In contrast, widely circulated video evidence showed the Minneapolis killings in horrifying detail. Within days of each shooting, news organizations had compiled detailed visual timelines, often based on recordings by protesters and observers, that sharply contradicted government accounts of what happened to Good and Pretti.

Finally, consider two popular protest songs that emerged from Kent State and Minneapolis: Crosby, Stills, Nash & Young’s “Ohio” and Bruce Springsteen’s “Streets of Minneapolis.”

Crosby, Stills, Nash & Young recorded, pressed and released “Ohio” with remarkable speed for 1970. The vinyl single reached record stores and radio stations on June 4, a month after the Kent State shootings. The song peaked at No. 14 on the Billboard chart two months later.

Neil Young’s lyrics described the Kent State events in mythic terms, warning of “tin soldiers” and telling young Americans: “We’re finally on our own.” Young did not describe the shootings in detail. The song does not name Kent State, the National Guard or the fallen students. Instead, it presents the events as symbolic of a broader generational conflict over the Vietnam War.

Springsteen released “Streets of Minneapolis” on Jan. 28, 2026 – just four days after CBP agents killed Pretti. Two days later, the song topped streaming charts worldwide.

The internet and social media let Springsteen document Minneapolis, almost in real time, for a mass audience. Springsteen’s lyrics balance symbolism with specificity, naming not just “King Trump” but also victims Pretti and Good, key Trump officials Stephen Miller and Kristi Noem, main Minneapolis artery Nicollet Avenue, and the protesters’ “whistles and phones,” before fading on a chant of “ICE out!”

Critics offer compelling arguments that 21st-century mass communication degrades social relationships, elections and culture. In Minneapolis, disinformation has muddied crucial facts about the protests and killings.

At the same time, Minneapolis has shown how networked communication can promote free speech. Through focused protests, recordings of government action, and viral popular culture, today’s public can get fuller, clearer information to help critically assess government actions.

Gregory P. Magarian does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Why the ‘Streets of Minneapolis’ have echoed with public support – unlike the campus of Kent State in 1970 – https://theconversation.com/why-the-streets-of-minneapolis-have-echoed-with-public-support-unlike-the-campus-of-kent-state-in-1970-274917

Source: The Conversation – USA – By Matthew Bunn, Professor of the Practice of Energy, National Security and Foreign Policy, Harvard Kennedy School

For the first time in more than half a century, there are no binding restraints on the buildup of the largest nuclear forces on Earth. The New START treaty expired on Feb. 5, 2026, ending the last agreed limits on U.S. and Russian nuclear forces.

New START limited the number of strategic nuclear weapons the United States and Russia could deploy to 1,550 each. It also limited the missiles and bombers those warheads were loaded on, required on-site inspections and data exchanges, barred interference with satellite monitoring, and established a joint commission to discuss disputes. It did not limit the number of nuclear weapons each side could hold in reserve.

With China rapidly building up its nuclear forces, intense rivalry between the United States, China and Russia, and evolving technologies – from precision conventional weapons to artificial intelligence complicating nuclear balances – there is a real potential of an unpredictable three-way nuclear arms competition.

Such a competition could increase the danger of nuclear conflict, which I believe is higher than it has been in decades.

The security of agreed restraint

While the particular numbers of warheads and delivery vehicles an accord specifies may not make an immense difference, nuclear agreements offer important advantages in four key areas:

• Predictability, limiting the pressures to build up nuclear arsenals that come from worst-case analysis of what adversaries might build and the destabilization that unexpected new weapons can bring.

• Transparency, elements such as data exchanges, on-site inspections and limits on interfering with satellite monitoring, giving each side a better ability to understand what is going on with the others’ nuclear forces.

• Reduced first-strike incentives, from banning or limiting particularly dangerous types of weapons.

• Improved relations, through the mere fact that the other side is willing to limit the nuclear forces arrayed against you, which undermines the belief that they are implacably bent on your utter destruction. This reduces the intensity of hostility that can drive crises and escalation.

After 1962’s Cuban missile crisis, President John F. Kennedy realized that relying on nuclear deterrence without any agreed nuclear restraints or risk-reduction measures is just too dangerous. He moved quickly to negotiate the Limited Test Ban Treaty in 1963 and put in place a U.S.-Soviet hotline for crisis communication.

He also launched a series of initiatives that led to reductions in defense spending on both sides, cuts in production of nuclear materials for weapons, and even troop pullbacks in Europe. Every subsequent U.S. president has pursued nuclear arms control accords.

Moreover, the countries that have promised not to get nuclear weapons under the Nuclear Nonproliferation Treaty want to see the nuclear-armed nations living up to their treaty obligation to negotiate in good faith toward nuclear disarmament. As pressure builds for countries to get their own nuclear weapons, maintaining the nonproliferation regime and getting the non-nuclear countries’ votes for stronger nuclear safeguards or export controls is likely to require the nuclear-armed nations to accept at least some constraints of their own.

Critics of arms control point out that Russia has violated many past accords – and the Trump administration has accused both Russia and China of carrying out illicit nuclear tests, though his administration has not offered solid evidence in public so far. But despite these very real issues, key elements of these agreements were implemented, and they “left the United States safer,” as Secretary of State Marco Rubio has noted. More than four-fifths of the nuclear weapons that used to exist in the world have been dismantled.

New limits or buildup?

So, what’s next? President Donald Trump ignored Russian President Vladimir Putin’s proposal that both sides stay within the limits of New START while they explored options for new steps. But Trump said he wants to negotiate a “better” deal on fewer nuclear weapons – a deal that would not only limit U.S. and Russian strategic forces but also China’s much smaller but rapidly growing nuclear forces and Russia’s large force of nonstrategic nuclear weapons – that is, ones for battlefield or regional use.

So far, though, no negotiations on follow-on accords are underway, and the administration has not offered to negotiate about any of the U.S. weapons systems that worry Russia and China.

Moreover, there is strong pressure in Washington to build up U.S. nuclear forces rather than reduce them, to deter both Russia and China – while also dealing with the smaller but still dangerous North Korean nuclear force. The United States has many hundreds of nuclear weapons in storage that could be brought out and put on existing missiles, along with empty missile tubes on submarines that could again be filled with missiles. And the U.S. is developing new weapons, such as a nuclear-armed, sea-launched cruise missile.

Constraints and challenges

In my view, the more than 1,500 strategic nuclear weapons the United States already has deployed – with a major modernization underway – provide a sufficient deterrent to aggression. And if the United States begins to build up, Russia will respond in kind, and China may go even further. Once a multisided buildup is underway, its momentum will be more difficult to reverse.

Fortunately, the United States, Russia and China all have strong national interests in avoiding an unrestrained nuclear race, which would leave all of them poorer and no more secure. While the United States has quite a few nuclear weapons in storage, its nuclear modernization is struggling with enormous delays and cost overruns, and its industrial base is simply not prepared for a major nuclear expansion.

Putin is building a war economy that can churn out a lot of weapons – but he knows his economy is a 10th the size of the U.S.’s, and he wants to focus on rebuilding the conventional forces being chewed up in his war on Ukraine, making nuclear competition a bad idea. China has an economy to match the U.S.’s and an unrivaled manufacturing capacity, but it, too, would be worse off if its buildup provokes a U.S. buildup in response and a collapse of nuclear restraints.

Despite these common interests, finding a path to new accords among at least three parties, rather than two, will not be easy. Coalitions in each capital will have to win arguments that an accord is in their nation’s interest at the same time. The parties will have to address in some way the non-nuclear technologies that affect nuclear balances, and technologies such as cyber weapons and artificial intelligence would be hard to count or verify.

U.S. political polarization might make it very difficult to get a two-thirds vote in the Senate to ratify a treaty – though there are many other possible approaches, from reciprocal political commitments to executive agreements.

Famously unpredictable, Trump might still reverse course and agree to some version of Putin’s proposal for a “strategic pause” in which neither the United States nor Russia would build up its nuclear capabilities for the time being, while talks on next steps were underway. That would have the advantage of offering time to explore the options before new nuclear buildups got locked in.

And that would give him more chance of reaching his oft-stated goal of being the one to bring home a deal to reduce nuclear weapons and the dangers they pose.

Matthew Bunn is a member of the Board of Directors of the Arms Control Association; is a member of the Committee on International Security and Arms Control of the National Academy of Sciences; has consulted for several U.S. national laboratories; and has served on the Academic Alliance of U.S. Strategic Command.

– ref. Last nuclear weapons limits expired – pushing world toward new arms race – https://theconversation.com/last-nuclear-weapons-limits-expired-pushing-world-toward-new-arms-race-275749

Source: The Conversation – USA (2) – By Nir Eisikovits, Professor of Philosophy and Director, Applied Ethics Center, UMass Boston

Public debate about artificial intelligence in higher education has largely orbited a familiar worry: cheating. Will students use chatbots to write essays? Can instructors tell? Should universities ban the tech? Embrace it?

These concerns are understandable. But focusing so much on cheating misses the larger transformation already underway, one that extends far beyond student misconduct and even the classroom.

Universities are adopting AI across many areas of institutional life. Some uses are largely invisible, like systems that help allocate resources, flag “at-risk” students, optimize course scheduling or automate routine administrative decisions. Other uses are more noticeable. Students use AI tools to summarize and study, instructors use them to build assignments and syllabuses and researchers use them to write code, scan literature and compress hours of tedious work into minutes.

People may use AI to cheat or skip out on work assignments. But the many uses of AI in higher education, and the changes they portend, beg a much deeper question: As machines become more capable of doing the labor of research and learning, what happens to higher education? What purpose does the university serve?

Over the past eight years, we’ve been studying the moral implications of pervasive engagement with AI as part of a joint research project between the Applied Ethics Center at UMass Boston and the Institute for Ethics and Emerging Technologies. In a recent white paper, we argue that as AI systems become more autonomous, the ethical stakes of AI use in higher ed rise, as do its potential consequences.

As these technologies become better at producing knowledge work – designing classes, writing papers, suggesting experiments and summarizing difficult texts – they don’t just make universities more productive. They risk hollowing out the ecosystem of learning and mentorship upon which these institutions are built, and on which they depend.

Nonautonomous AI

Consider three kinds of AI systems and their respective impacts on university life:

AI-powered software is already being used throughout higher education in admissions review, purchasing, academic advising and institutional risk assessment. These are considered “nonautonomous” systems because they automate tasks, but a person is “in the loop” and using these systems as tools.

These technologies can pose a risk to students’ privacy and data security. They also can be biased. And they often lack sufficient transparency to determine the sources of these problems. Who has access to student data? How are “risk scores” generated? How do we prevent systems from reproducing inequities or treating certain students as problems to be managed?

These questions are serious, but they are not conceptually new, at least within the field of computer science. Universities typically have compliance offices, institutional review boards and governance mechanisms that are designed to help address or mitigate these risks, even if they sometimes fall short of these objectives.

Hybrid AI

Hybrid systems encompass a range of tools, including AI-assisted tutoring chatbots, personalized feedback tools and automated writing support. They often rely on generative AI technologies, especially large language models. While human users set the overall goals, the intermediate steps the system takes to meet them are often not specified.

Hybrid systems are increasingly shaping day-to-day academic work. Students use them as writing companions, tutors, brainstorming partners and on-demand explainers. Faculty use them to generate rubrics, draft lectures and design syllabuses. Researchers use them to summarize papers, comment on drafts, design experiments and generate code.

This is where the “cheating” conversation belongs. With students and faculty alike increasingly leaning on technology for help, it is reasonable to wonder what kinds of learning might get lost along the way. But hybrid systems also raise more complex ethical questions.

One has to do with transparency. AI chatbots offer natural-language interfaces that make it hard to tell when you’re interacting with a human and when you’re interacting with an automated agent. That can be alienating and distracting for those who interact with them. A student reviewing material for a test should be able to tell if they are talking with their teaching assistant or with a robot. A student reading feedback on a term paper needs to know whether it was written by their instructor. Anything less than complete transparency in such cases will be alienating to everyone involved and will shift the focus of academic interactions from learning to the means or the technology of learning. University of Pittsburgh researchers have shown that these dynamics bring forth feelings of uncertainty, anxiety and distrust for students. These are problematic outcomes.

A second ethical question relates to accountability and intellectual credit. If an instructor uses AI to draft an assignment and a student uses AI to draft a response, who is doing the evaluating, and what exactly is being evaluated? If feedback is partly machine-generated, who is responsible when it misleads, discourages or embeds hidden assumptions? And when AI contributes substantially to research synthesis or writing, universities will need clearer norms around authorship and responsibility – not only for students, but also for faculty.

Finally, there is the critical question of cognitive offloading. AI can reduce drudgery, and that’s not inherently bad. But it can also shift users away from the parts of learning that build competence, such as generating ideas, struggling through confusion, revising a clumsy draft and learning to spot one’s own mistakes.

Autonomous agents

The most consequential changes may come with systems that look less like assistants and more like agents. While truly autonomous technologies remain aspirational, the dream of a researcher “in a box” – an agentic AI system that can perform studies on its own – is becoming increasingly realistic.

Agentic tools are anticipated to “free up time” for work that focuses on more human capacities like empathy and problem-solving. In teaching, this may mean that faculty may still teach in the headline sense, but more of the day-to-day labor of instruction can be handed off to systems optimized for efficiency and scale. Similarly, in research, the trajectory points toward systems that can increasingly automate the research cycle. In some domains, that already looks like robotic laboratories that run continuously, automate large portions of experimentation and even select new tests based on prior results.

At first glance, this may sound like a welcome boost to productivity. But universities are not information factories; they are systems of practice. They rely on a pipeline of graduate students and early-career academics who learn to teach and research by participating in that same work. If autonomous agents absorb more of the “routine” responsibilities that historically served as on-ramps into academic life, the university may keep producing courses and publications while quietly thinning the opportunity structures that sustain expertise over time.

The same dynamic applies to undergraduates, albeit in a different register. When AI systems can supply explanations, drafts, solutions and study plans on demand, the temptation is to offload the most challenging parts of learning. To the industry that is pushing AI into universities, it may seem as if this type of work is “inefficient” and that students will be better off letting a machine handle it. But it is the very nature of that struggle that builds durable understanding. Cognitive psychology has shown that students grow intellectually through doing the work of drafting, revising, failing, trying again, grappling with confusion and revising weak arguments. This is the work of learning how to learn.

Taken together, these developments suggest that the greatest risk posed by automation in higher education is not simply the replacement of particular tasks by machines, but the erosion of the broader ecosystem of practice that has long sustained teaching, research and learning.

An uncomfortable inflection point

So what purpose do universities serve in a world in which knowledge work is increasingly automated?

One possible answer treats the university primarily as an engine for producing credentials and knowledge. There, the core question is output: Are students graduating with degrees? Are papers and discoveries being generated? If autonomous systems can deliver those outputs more efficiently, then the institution has every reason to adopt them.

But another answer treats the university as something more than an output machine, acknowledging that the value of higher education lies partly in the ecosystem itself. This model assigns intrinsic value to the pipeline of opportunities through which novices become experts, the mentorship structures through which judgment and responsibility are cultivated, and the educational design that encourages productive struggle rather than optimizing it away. Here, what matters is not only whether knowledge and degrees are produced, but how they are produced and what kinds of people, capacities and communities are formed in the process. In this version, the university is meant to serve as no less than an ecosystem that reliably forms human expertise and judgment.

In a world where knowledge work itself is increasingly automated, we think universities must ask what higher education owes its students, its early-career scholars and the society it serves. The answers will determine not only how AI is adopted, but also what the modern university becomes.

The Applied Ethics Center at UMass Boston receives funding from the Institute for Ethics and Emerging Technologies. Nir Eisikovits serves as the data ethics advisor to MindGuard, a startup focused on AI integration into companies’ workflow.

Jacob Burley receives funding from The Applied Ethics Center at UMass Boston.

– ref. The greatest risk of AI in higher education isn’t cheating – it’s the erosion of learning itself – https://theconversation.com/the-greatest-risk-of-ai-in-higher-education-isnt-cheating-its-the-erosion-of-learning-itself-270243